library(magrittr)
library(tidyverse)
library(Seurat)
library(readxl)
library(cowplot)
library(colorblindr)
library(viridis)
library(progeny)

theme_cowplot2 <- function(...) {
  theme_cowplot(font_size = 12, ...) %+replace%
    theme(strip.background = element_blank(),
          plot.background = element_blank())
}
theme_set(theme_cowplot2())
coi <- params$cell_type
cell_sort <- params$cell_sort
cell_type_major <- params$cell_type_major
louvain_resolution <- params$louvain_resolution
louvain_cluster <- params$louvain_cluster

1 Cluster markers

1.1 Major Ovarian.cancer.super markers for cell assign

### load all data ---------------------------------
source("_src/global_vars.R")

# seu_obj <- read_rds(paste0("/work/shah/isabl_data_lake/analyses/16/52/1652/celltypes/", coi, "_processed.rds"))
seu_obj <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/Ovarian.cancer.cell_highqc.rds"))

myfeatures <- c("umapharmony_1", "umapharmony_2", "sample", louvain_cluster, "doublet", "nCount_RNA", "nFeature_RNA", "percent.mt", "doublet_score")

plot_data_wrapper <- function(cluster_res) {
  cluster_res <- enquo(cluster_res)
  as_tibble(FetchData(seu_obj, myfeatures)) %>% 
    left_join(meta_tbl, by = "sample") %>% 
    rename(cluster = !!cluster_res) %>% 
    mutate(cluster = as.character(cluster),
           tumor_supersite = ordered(tumor_supersite, levels = names(clrs$tumor_supersite)))
}

plot_data <- plot_data_wrapper(louvain_cluster)

1.2 Subtype currated markers

helper_f <- function(x) ifelse(is.na(x), "", x)

markers_v6_super[[coi]] %>% 
  group_by(subtype) %>% 
  mutate(rank = row_number(gene)) %>% 
  spread(subtype, gene) %>% 
  mutate_all(.funs = helper_f) %>% 
  formattable::formattable()

rank	Cancer.cell.1	Cancer.cell.2	Cancer.cell.3	Cancer.cell.4	Cancer.cell.5	Cancer.cell.6	Ciliated.cell.1	Ciliated.cell.2	Ciliated.cell.3	Cycling.cancer.cell.1	Cycling.cancer.cell.2	doublet.Immune.cell
1	DAPL1	IGFBP3	CXCL10	COL1A1	CCND1	FTL	C20orf85	C5orf49	ATP5F1E	CENPF	ATAD2	CD52
2	FOLR1	KRT17	IFI6	COL1A2	CRISP3	HSPA6	CAPS	CFAP126	DEFB1	HMGB2	H2AFZ	IGHG1
3	SCGB1D2	KRT7	IFIT1	COL3A1	OVGP1	MGP	CAPSL	DNALI1	GNAS	MKI67	HIST1H4C	IGKC
4	SCGB2A1	LCN2	IFIT2	DCN	PLCG2	RACK1	PIFO	PIFO	GPX3	PTTG1	PCLAF	IGLC3
5	SNHG19	MMP7	IFIT3	FN1	PTMS	SCX	TPPP3	RSPH1	IMPA2	TOP2A	TUBA1B	IL7R
6		S100A10	ISG15	SPARC	SNHG9	ZFAS1						PTPRC
7		S100A9	MX1
8		SLPI
9		TACSTD2

1.3 Subtype cluster markers

## define patient specific clusters
patient_specific_clusters <- plot_data %>% 
  group_by(patient_id_short, cluster) %>% 
  tally %>% 
  group_by(cluster) %>% 
  mutate(nrel = n / sum(n),
         ntotal = sum(n)) %>% 
  arrange(desc(nrel)) %>% 
  distinct(cluster, .keep_all = T) %>% 
  filter(nrel > 0.5) %>% 
  ungroup %>% 
  mutate(cluster_label_ps = make.names(paste0("OV.", patient_id_short, ".specific"), unique = T),
         cluster = as.numeric(cluster)) %>% 
  distinct(cluster, cluster_label_ps)


## Hypergeometric test --------------------------------------

marker_tbl <- read_tsv("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/Ovarian.cancer.cell_highqc_markers_02.tsv")

test_set <- marker_tbl %>% 
  group_by(cluster) %>% 
  filter(!str_detect(gene, "^RPS|^RPL")) %>% 
  slice(1:50) %>% 
  mutate(k = length(cluster)) %>% 
  ungroup %>%
  select(cluster, gene, k) %>% 
  mutate(join_helper = 1) %>% 
  group_by(cluster, join_helper, k) %>%
  nest(test_set = gene)

markers_doub_tbl <- markers_v6 %>% 
  enframe("subtype", "gene") %>% 
  filter(!(subtype %in% unique(c(coi, cell_type_major)))) %>% 
  unnest(gene) %>% 
  group_by(gene) %>% 
  filter(length(gene) == 1) %>% 
  mutate(subtype = paste0("doublet.", subtype)) %>% 
  bind_rows(tibble(subtype = "Mito.high", gene = grep("^MT-", rownames(seu_obj), value = T)))

ref_set <- markers_v6_super[[coi]] %>% 
  bind_rows(markers_doub_tbl) %>% 
  group_by(subtype) %>% 
  mutate(m = length(gene),
         n = length(rownames(seu_obj))-m,
         join_helper = 1) %>% 
  group_by(subtype, m, n, join_helper) %>%
  nest(ref_set = gene)

hyper_tbl <- test_set %>% 
  left_join(ref_set, by = "join_helper") %>% 
  group_by(cluster, subtype, m, n, k) %>%
  do(q = length(intersect(unlist(.$ref_set), unlist(.$test_set)))) %>%
  mutate(pval = 1-phyper(q = q, m = m, n = n, k = k)) %>%
  ungroup %>%
  mutate(qval = p.adjust(pval, "BH"),
         sig = qval < 0.01)

# hyper_tbl %>% 
#   group_by(subtype) %>% 
#   filter(any(qval < 0.01)) %>%
#   ggplot(aes(subtype, -log10(qval), fill = sig)) +
#   geom_bar(stat = "identity") +
#   facet_wrap(~cluster) +
#   coord_flip()
  
low_rank <- str_detect(unique(hyper_tbl$subtype), "doublet|dissociated")
subtype_lvl <- c(sort(unique(hyper_tbl$subtype)[!low_rank]), sort(unique(hyper_tbl$subtype)[low_rank]))

cluster_label_tbl <- hyper_tbl %>% 
  mutate(subtype = ordered(subtype, levels = subtype_lvl)) %>% 
  arrange(qval, subtype) %>%
  group_by(cluster) %>% 
  slice(1) %>% 
  mutate(subtype = ifelse(sig, as.character(subtype), paste0("unknown_", cluster))) %>% 
  select(cluster, cluster_label = subtype) %>% 
  ungroup %>% 
  mutate(cluster_label = make.unique(cluster_label, sep = "_")) %>% 
  left_join(patient_specific_clusters, by = "cluster") %>% 
  mutate(cluster_label = ifelse(is.na(cluster_label_ps), cluster_label, cluster_label_ps)) %>% 
  select(-cluster_label_ps)

seu_obj$cluster_label <- unname(deframe(cluster_label_tbl)[as.character(unlist(seu_obj[[paste0("RNA_snn_res.", louvain_resolution)]]))])
plot_data$cluster_label <- seu_obj$cluster_label

cluster_n_tbl <- seu_obj$cluster_label %>% 
  table() %>% 
  enframe("cluster_label", "cluster_n") %>% 
  mutate(cluster_nrel = cluster_n/sum(cluster_n))

marker_sheet <- marker_tbl %>% 
  left_join(cluster_label_tbl, by = "cluster") %>% 
  group_by(cluster_label) %>% 
  filter(!str_detect(gene, "^RPS|^RPL")) %>% 
  slice(1:50) %>% 
  mutate(rank = row_number(-avg_logFC)) %>% 
  select(cluster_label, gene, rank) %>% 
  ungroup %>% 
  mutate(cluster_label = ordered(cluster_label, levels = unique(c(names(clrs$cluster_label[[coi]]), sort(cluster_label))))) %>% 
  spread(cluster_label, gene) %>% 
  mutate_all(.funs = helper_f)

marker_tbl_annotated <- marker_tbl %>% 
  left_join(cluster_label_tbl, by = "cluster") %>% 
  left_join(cluster_n_tbl, by = "cluster_label") %>% 
  select(-cluster) %>% 
  mutate(cluster_label = ordered(cluster_label, levels = unique(c(names(clrs$cluster_label[[coi]]), sort(cluster_label))))) %>% 
  arrange(cluster_label, -avg_logFC, p_val_adj)

write_tsv(marker_sheet, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_marker_sheet.tsv"))
write_tsv(marker_sheet, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/supplementary_tables/", coi, "_marker_sheet.tsv"))

write_tsv(marker_tbl_annotated, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_marker_table_annotated.tsv"))
write_tsv(marker_tbl_annotated, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/supplementary_tables/", coi, "_marker_table_annotated.tsv"))

formattable::formattable(marker_sheet)

rank	Cancer.cell.1	Cancer.cell.2	Cancer.cell.3	Cancer.cell.4	Cycling.cancer.cell.1	Cycling.cancer.cell.2	Ciliated.cell.1	Ciliated.cell.2	doublet.Immune.cell	OV.065.specific	OV.082.specific	OV.089.specific
1	DAPL1	S100A9	ISG15	COL1A1	CENPF	HIST1H4C	CAPS	CFAP126	IGKC	OVGP1	HSPA6	GPX3
2	SCGB2A1	KRT17	CXCL10	COL3A1	PTTG1	TUBA1B	C20orf85	PIFO	IGLC3	PLCG2	FTL	GNAS
3	SCGB1D2	LCN2	IFIT3	COL1A2	UBE2S	PCLAF	TPPP3	DNALI1	LTB	CRISP3	RACK1	ATP5F1E
4	SNHG19	TACSTD2	IFIT1	FN1	HMGB2	ATAD2	C9orf24	RSPH1	IGHG1	SNHG9	ZFAS1	DEFB1
5	FOLR1	IGFBP3	MX1	SPARC	TPX2	H2AFZ	RSPH1	C5orf49	IL7R	CCND1	SCX	IMPA2
6	CRABP1	SLPI	IFI6	DCN	ASPM	PCNA	C5orf49	LRRIQ1	CD52	PTMS	MGP	GSTM3
7	PTGDS	S100A10	IFIT2	LUM	MKI67	CKS1B	TMEM190	FAM183A	PTPRC	PEG10	IGFBP7	PSMA7
8	THSD4	KRT7	IFI44L	IGFBP7	CCNB1	TK1	PIFO	C9orf24	GIMAP7	DSTN	NPM1	ANXA4
9	APOA1	RARRES1	PARP14	COL6A1	CDC20	MKI67	FAM183A	CASC1	SRGN	YWHAE	CNBP	FTL
10		GPRC5A	TNFSF10	COL6A2	TOP2A	TYMS	C1orf194	C20orf85	IGHG3	SNHG19	FKBP4	CAPS
11		MMP7	STAT1	CCDC80	CKS2	TUBB	CETN2	ARMC3	IGLC2	APOLD1	EEF1D	IGFBP7
12		CRYAB	IFI27	CTHRC1	UBE2C	HMGB2	ODF3B	C1orf194	BTG1	MYL12B	CDV3	PCLAF
13		FTH1	RSAD2	CALD1	ARL6IP1	DUT	TXN	TPPP3	CCR7	PTPRA	GAPDH	PLIN2
14		NDRG1	OAS1	VIM	BIRC5	TOP2A	AGR3	CFAP45	KLF2	WBP11	OAZ1	PFDN4
15		KRT19	XAF1	COL6A3	LGALS1	DEK	CFAP126	EFCAB1	BIRC3	SNHG25	YBX3	C12orf75
16		S100A11	GBP1	NNMT	CCNB2	SMC4	IGFBP7	DNAAF1	CD3D	RBMX	YBX1	RHOBTB3
17		VEGFA	SAMD9	SPARCL1	TUBA1B	MCM7	CAPSL	ENKUR	RGS1	EPB41L2	LTC4S	CDKN2C
18		ANXA1	IFIH1	LGALS1	CEP55	UBE2C	C11orf88	CEP126	SPP1	RBM8A	CYC1	BMP7
19		FTL	GBP4	MGP	CDKN3	HELLS	MORN2	HYDIN	IL32	PABPC4	NOP53	EIF3K
20		SLC2A1	PSMB9	TAGLN	NUSAP1	CDK1	TUBB4B	PPIL6	CD48	METAP2	EEF2	XAGE3
21		SOX4	DDX58	MMP11	JPT1	MCM3	TUBA1A	CAPSL	FYB1	AMD1	EDN1	DSTN
22		S100A6	LY6E	VCAN	H2AFZ	CENPF	LRRIQ1	ZMYND10	TRBC2	STRAP	EIF4A2	MSRB1
23		TFPI2	ISG20	ACTA2	NMU	STMN1	FOXJ1	TEKT2	IKZF1	BRD9	TSTA3	EIF1
24		PI3	OAS3	RARRES2	KPNA2	MYBL2	ZMYND10	CFAP43	CORO1A	CREBZF	PRELID1	ATP5PO
25		C15orf48	HLA-B	COL5A2	HMMR	CLSPN	DNAAF1	DYDC2	CD3G	MYL6B	SLU7	RDH10
26		NEAT1	CXCL11	AEBP1	PTMS	UBE2T	C9orf116	CFAP44	B2M	PPT1	CANX	COX6B1
27		S100A2	RNF213	POSTN	HMGN2	NASP	EFCAB1	CDC20B	CD2	SNRPB	CTTNBP2	SDC2
28		TNFRSF12A	LAP3	PLA2G2A	ECT2	MCM4	EFHC1	CFAP54	GMFG	MYL12A	TTC1	SFTA2
29		C19orf33	IFI16	ERP27	SMC4	SMC2	DNALI1	FAM81B	RIPOR2	TMCO1	CLINT1	ORM1
30		TCIM	TAP1	NBL1	HMGB1	NUSAP1	DYDC2	SPATA17	ETS1	SNRPB2	TMEM238	CNTD2
31		TAGLN	IDO1	COL8A1	CENPE	HMGB1	IK	TEKT1	GIMAP4	NAP1L1	COX4I1	HOXB-AS3
32		MAL2	HLA-A	TIMP3	STMN1	RAD51AP1	CEP126	SPEF1	HCST	ST13	ZNF787	PTH1R
33		LGALS3	PLSCR1	LRRC75A	ANP32E	ZWINT	SNTN	CFAP46	TRAC	PTX3	CLTB	CYS1
34		NUPR1	HERC5	IGFBP4	CKS1B	TPX2	AL357093.2	DNAH7	TRBC1	MIR4458HG	HIGD2A	GLRX
35		ANXA2	IFI44	CDH6	KIF20B	DNMT1	ROPN1L	CFAP73	TMSB4X	WDR70	NACA	EEF1A2
36		CST6	B2M	MMP2	RAD21	RRM2	MLF1	ZBBX	CXCR4	WDR45B	UQCRB	ERVMER34-1
37		EMP1	HLA-C	BGN	NUCKS1	E2F1	PSENEN	ROPN1L	LAPTM5	PA2G4	ATP5PB	CA2
38		IL32	OAS2	TPM1	HMGB3	CENPW	ERICH3	EFCAB10	CD3E	NMT1	RSPO3	LINC02532
39		SQSTM1	BST2	IGFBP6	TROAP	RANBP1	CCDC170	WDR38	C1QB	TTYH1	RSL1D1	NCCRP1
40		CAST	WARS	C1R	DLGAP5	MAD2L1	TEKT2	SPAG8	LCP1	VIM	CYHR1	LAMB4
41		MUC4	SAMD9L	SPON2	AURKA	TMPO	PERP	DRC1	ARHGDIB	KRT81	UBE2D2	BICC1
42		TGM2	EIF2AK2	COL5A1	H2AFV	CENPX	SPA17	CFAP157	CD37	IGFBP3	S100A1	MIOX
43		SAT1	UBE2L6	KRT7	TUBB4B	FANCI	C9orf135	RSPH4A	TYROBP	NSA2	SRP72	ORM2
44		CLDN4	MX2	HTRA1	CENPA	TMEM106C	LRRC23	WDR49	CCL4	HSP90B1	SSB	CPNE8
45		FAM3C	OASL	CAV1	NCAPD2	DHFR	CFAP45	C7orf57	EMP3	HMGB1	PUF60	NID2
46		BIRC3	IFI35	SYT8	CENPW	PRKDC	ARMC3	DYNLRB2	LSP1	TMA7	BTF3	AC022784.1
47		LDHA	IFITM3	C1S	TUBA1C	HMGN2	WDR54	WDR63	IGHG4	ANO1	UQCRH	SLCO4A1
48		CAMK2N1	HLA-E	SERPINF1	NEK2	BIRC5	MNS1	MAP3K19	TRAF3IP3	PEG3	EIF3E	CR1L
49		ITGB8	RARRES3	ANXA2	NUF2	DTYMK	TEKT1	C6orf118	STK17B	CCND2	MAP2K2	RBP7
50		MUC16	NUPR1	CFH	MAD2L1	ASPM	TUBA4B	DNAH12	ACAP1	CD177	LINC02308	PCAT19

2 Clusters

2.1 sizes

enframe(sort(table(seu_obj$cluster_label))) %>% 
  mutate(name = ordered(name, levels = rev(name))) %>% 
  ggplot() +
  geom_bar(aes(name, value), stat = "identity") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  labs(y = c("#cells"), x = "cluster")

2.2 UMAP

alpha_lvl <- ifelse(nrow(plot_data) < 20000, 0.2, 0.1)
pt_size <- ifelse(nrow(plot_data) < 20000, 0.2, 0.05)

common_layers_disc <- list(  
  geom_point(size = pt_size, alpha = alpha_lvl),
  NoAxes(),
  guides(color = guide_legend(override.aes = list(size = 2, alpha = 1))),
  labs(color = "")
)

common_layers_cont <- list(  
  geom_point(size = pt_size, alpha = alpha_lvl),
  NoAxes(),
  scale_color_gradientn(colors = viridis(9)),
  guides(color = guide_colorbar())
)

ggplot(plot_data, aes(umapharmony_1, umapharmony_2, color = cluster_label)) + 
  common_layers_disc +
  #facet_wrap(~therapy) +
  ggtitle("Sub cluster")

3 Filtering out doublet and mito high clusters

3.1 UMAP

my_subtypes <- names(clrs$cluster_label[[coi]])

my_subtypes <- c(my_subtypes, unlist(lapply(paste0("_", 1:3), function(x) paste0(my_subtypes, x)))) %>% .[!str_detect(., "doublet")]

if (cell_sort == "CD45+") {
cells_to_keep <- colnames(seu_obj)[seu_obj$cluster_label %in% my_subtypes & !(str_detect(seu_obj$cell_id, "CD45N"))]
}

if (cell_sort == "CD45-") {
cells_to_keep <- colnames(seu_obj)[seu_obj$cluster_label %in% my_subtypes & !(str_detect(seu_obj$cell_id, "CD45P"))]
}

seu_obj_sub <- subset(seu_obj, cells = cells_to_keep)
seu_obj_sub <- RunUMAP(seu_obj_sub, dims = 1:50, reduction = "harmony", reduction.name = "umapharmony", reduction.key = "umapharmony_")
seu_obj_sub$cluster_label <- seu_obj$cluster_label[colnames(seu_obj) %in% colnames(seu_obj_sub)]
write_rds(seu_obj_sub, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered.rds"))
seu_obj_sub <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered.rds"))

plot_data_sub <- as_tibble(FetchData(seu_obj_sub, c(myfeatures, "cluster_label"))) %>% 
  left_join(meta_tbl, by = "sample") %>% 
  mutate(cell_id = colnames(seu_obj_sub),
         cluster_label = ordered(cluster_label, levels = my_subtypes))
  

ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = cluster_label)) + 
  common_layers_disc +
  scale_color_manual(values = clrs$cluster_label[[coi]]) +
  #facet_wrap(~cluster_label) +
  ggtitle("Sub cluster")

ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = patient_id_short)) + 
  common_layers_disc +
  ggtitle("Patient") +
  # facet_wrap(~therapy) +
  scale_color_manual(values = clrs$patient_id_short)

ggplot(plot_data_sub, aes(umapharmony_1, umapharmony_2, color = tumor_supersite)) + 
  # geom_point(aes(umapharmony_1, umapharmony_2), 
  #            color = "grey90", size = 0.01, 
  #            data = select(plot_data_sub, -tumor_supersite)) +
  common_layers_disc +
  ggtitle("Site") +
  # facet_wrap(~therapy) +
  scale_color_manual(values = clrs$tumor_supersite)

write_tsv(select(plot_data_sub, cell_id, everything(), -contains("RNA_")), paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_embedding.tsv"))

3.2 add module scores and pathway scores

signature_modules_cd8 <- read_excel("_data/small/signatures/SPECTRUM v5 sub cluster markers.xlsx", sheet = 2, skip = 1, range = "M2:P100") %>% 
  gather(module, gene) %>% 
  na.omit() %>% 
  group_by(module) %>% 
  do(gene = c(.$gene)) %>% 
  {setNames(.$gene, .$module)} %>% 
  setNames(paste0(names(.), ".module"))

signature_modules_cin <- yaml::read_yaml("_data/small/signatures/cgas_sting.yaml") %>% 
  .[lapply(., length) > 1] %>% 
  setNames(paste0(names(.), ".module"))

## compute expression module scores
for (i in 1:length(signature_modules_cd8)) {
  seu_obj_sub <- AddModuleScore(seu_obj_sub, features = signature_modules_cd8[i], name = names(signature_modules_cd8)[i])
  seu_obj_sub[[names(signature_modules_cd8)[i]]] <- seu_obj_sub[[paste0(names(signature_modules_cd8)[i], "1")]]
  seu_obj_sub[[paste0(names(signature_modules_cd8)[i], "1")]] <- NULL
  print(paste(names(signature_modules_cd8)[i], "DONE"))
}

## [1] "CD8.Cytotoxic.module DONE"
## [1] "CD8.Dysfunctional.module DONE"
## [1] "CD8.Naive.module DONE"
## [1] "CD8.Predysfunctional.module DONE"

for (i in 1:length(signature_modules_cin)) {
  seu_obj_sub <- AddModuleScore(seu_obj_sub, features = signature_modules_cin[i], name = names(signature_modules_cin)[i])
  seu_obj_sub[[names(signature_modules_cin)[i]]] <- seu_obj_sub[[paste0(names(signature_modules_cin)[i], "1")]]
  seu_obj_sub[[paste0(names(signature_modules_cin)[i], "1")]] <- NULL
  print(paste(names(signature_modules_cin)[i], "DONE"))
}

## [1] "CIN.module DONE"
## [1] "CIN.responsive.Noncanonical.NFKB.targets.up.module DONE"
## [1] "CIN.responsive.Noncanonical.NFKB.targets.down.module DONE"
## [1] "Noncanonical.NFKB.regulators.up.module DONE"
## [1] "Noncanonical.NFKB.regulators.down.module DONE"
## [1] "Canonical.NFKB.regulators.module DONE"
## [1] "Interferon.regulators.module DONE"
## [1] "EMT.module DONE"
## [1] "Inflammation.module DONE"
## [1] "Motility.and.migration.module DONE"
## [1] "ISG.module DONE"
## [1] "SASP.module DONE"
## [1] "Myofibroblast.module DONE"
## [1] "Fibroblast.module DONE"
## [1] "Mesenchymal.stem.cell.module DONE"
## [1] "Pericytes.module DONE"
## [1] "Smooth.muscle.cell.module DONE"
## [1] "ER.stress.module DONE"
## [1] "IFNg.signaling.module DONE"

## compute progeny scores
progeny_list <- seu_obj_sub@assays$RNA@data[VariableFeatures(seu_obj_sub),] %>%
  as.matrix %>%
  progeny %>%
  as.data.frame %>%
  as.list

names(progeny_list) <- make.names(paste0(names(progeny_list), ".pathway"))

for (i in 1:length(progeny_list)) {
  seu_obj_sub <- AddMetaData(seu_obj_sub, metadata = progeny_list[[i]],
                             col.name = names(progeny_list)[i])
}

write_rds(seu_obj_sub, paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered_annotated.rds"))
seu_obj_sub <- read_rds(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_processed_filtered_annotated.rds"))

3.3 marker heatmap

marker_top_tbl <- marker_sheet[,-1] %>% 
  slice(1:10) %>% 
  as.list %>% 
  .[!str_detect(names(.), "doublet")] %>% 
  enframe("cluster_label_x", "gene") %>% 
  unnest(gene)

plot_data_markers <- as_tibble(FetchData(seu_obj_sub, c("cluster_label", myfeatures, unique(marker_top_tbl$gene)))) %>% 
  gather(gene, value, -c(1:(length(myfeatures)+1))) %>% 
  left_join(meta_tbl, by = "sample") %>% 
  mutate(cluster_label = ordered(cluster_label, levels = my_subtypes)) %>% 
  group_by(cluster_label, gene) %>% 
  summarise(value = mean(value, na.rm = T)) %>% 
  group_by(gene) %>% 
  mutate(value = scales::rescale(value)) %>% 
  left_join(marker_top_tbl, by = "gene") %>% 
  mutate(cluster_label_x = ordered(cluster_label_x, levels = rev(names(clrs$cluster_label[[coi]]))))

ggplot(plot_data_markers) +
  geom_tile(aes(gene, cluster_label, fill = value)) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  facet_grid(~cluster_label_x, scales = "free", space = "free") +
  scale_fill_gradientn(colors = viridis(9)) +
  labs(fill = "Scaled\nexpression") +
  theme(aspect.ratio = 1,
        axis.line = element_blank(),
        axis.ticks = element_blank(),
        axis.title = element_blank())

# ggsave(paste0("_fig/002_marker_heatmap_", coi, ".pdf"), width = nrow(marker_top_tbl)/6, height = 5)

3.4 composition

3.4.1 per site

comp_site_tbl <- plot_data_sub %>%
  filter(!is.na(tumor_supersite)) %>% 
  group_by(cluster_label, tumor_supersite) %>%
  tally %>%
  group_by(tumor_supersite) %>%
  mutate(nrel = n/sum(n)*100) %>%
  ungroup

pnrel_site <- ggplot(comp_site_tbl) +
  geom_bar(aes(tumor_supersite, nrel, fill = cluster_label),
           stat = "identity", position = position_stack()) +
  scale_y_continuous(expand = c(0, 0)) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  scale_fill_manual(values = clrs$cluster_label[[coi]]) +
  labs(fill = "Cluster", y = "Fraction [%]", x = "")

pnabs_site <- ggplot(comp_site_tbl) +
  geom_bar(aes(tumor_supersite, n, fill = cluster_label),
           stat = "identity", position = position_stack()) +
  scale_y_continuous(expand = c(0, 0)) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
  scale_fill_manual(values = clrs$cluster_label[[coi]]) +
  labs(fill = "Cluster", y = "# cells", x = "") 

plot_grid(pnabs_site, pnrel_site, ncol = 2, align = "h")

# ggsave(paste0("_fig/02_deep_dive_", coi, "_comp_site.pdf"), width = 8, height = 4)

3.4.2 per sample

comp_tbl_sample_sort <- plot_data_sub %>% 
  group_by(tumor_subsite, tumor_supersite, patient_id, consensus_signature, therapy, cluster_label) %>% 
  tally %>% 
  group_by(tumor_subsite, tumor_supersite, patient_id, consensus_signature, therapy) %>% 
  mutate(nrel = n/sum(n)*100,
         nsum = sum(n),
         log10n = log10(n),
         label_supersite = "Site",
         label_mutsig = "Signature",
         label_therapy = "Rx") %>% 
  ungroup %>% 
  arrange(desc(therapy), tumor_supersite) %>% 
  mutate(tumor_subsite_rx = paste0(tumor_subsite, "_", therapy)) %>% 
  mutate(tumor_subsite = ordered(tumor_subsite, levels = unique(tumor_subsite)),
         tumor_subsite_rx = ordered(tumor_subsite_rx, levels = unique(tumor_subsite_rx))) %>% 
  arrange(patient_id) %>% 
  mutate(label_patient_id = ifelse(as.logical(as.numeric(fct_inorder(as.character(patient_id)))%%2), "Patient1", "Patient2"))

sample_id_x_tbl <- plot_data_sub %>% 
  mutate(sort_short_x = cell_sort) %>% 
  distinct(patient_id, sort_short_x, tumor_subsite, therapy, sample) %>% 
  unite(sample_id_x, patient_id, sort_short_x, tumor_subsite, therapy) %>% 
  arrange(sample_id_x)

comp_tbl_sample_sort %>% 
  mutate(sort_short_x = cell_sort) %>% 
  unite(sample_id_x, patient_id, sort_short_x, tumor_subsite_rx) %>% 
  select(sample_id_x, cluster_label, n, nrel, nsum) %>% 
  left_join(sample_id_x_tbl, by = "sample_id_x") %>% 
  write_tsv(paste0("/work/shah/uhlitzf/data/SPECTRUM/freeze/v6/", coi, "_subtype_compositions.tsv"))

ybreaks <- c(500, 1000, 2000, 4000, 6000, 8000, 10000, 15000, 20000)

max_cells_per_sample <- max(comp_tbl_sample_sort$nsum)
ymaxn <- ybreaks[max_cells_per_sample < ybreaks][1]

comp_plot_wrapper <- function(y = "nrel", switch = NULL) {
  if (y == "nrel") ylab <- paste0("Fraction\nof cells [%]")
  if (y == "n") ylab <- paste0("Number\nof cells")
  p <- ggplot(comp_tbl_sample_sort, 
              aes_string("tumor_subsite_rx", y, fill = "cluster_label")) + 
    facet_grid(~patient_id, space = "free", scales = "free", switch = switch) +
    coord_cartesian(clip = "off") + 
    scale_fill_manual(values = clrs$cluster_label[[coi]]) + 
    theme(axis.text.x = element_blank(),
          axis.title.y = element_text(angle = 0, vjust = 0.5, hjust = 0.5, 
                                      margin = margin(0, -0.4, 0, 0, unit = "npc")),
          axis.ticks.x = element_blank(),
          axis.title.x = element_blank(),
          axis.line.x = element_blank(),
          strip.text.y = element_blank(),
          strip.text.x = element_blank(),
          strip.background.y = element_blank(),
          strip.background.x = element_blank(),
          plot.margin = margin(0, 0, 0, 0, "npc")) + 
    labs(x = "",
         y = ylab) +
    guides(fill = FALSE)
  if (y == "nrel") p <- p + 
    geom_bar(stat = "identity") +
    scale_y_continuous(expand = c(0, 0), 
                       breaks = c(0, 50, 100), 
                       labels = c("0", "50", "100"))
  if (y == "n") p <- p + 
    geom_bar(stat = "identity", position = position_stack()) +
    scale_y_continuous(expand = c(0, 0), 
                       breaks = c(0, ymaxn/2, ymaxn),
                       limits = c(0, ymaxn),
                       labels = c("", ymaxn/2, ymaxn)) +
    expand_limits(y = c(0, ymaxn)) +
    theme(panel.grid.major.y = element_line(linetype = 1, color = "grey90", size = 0.5))
  return(p)
} 

common_label_layers <- list(
  geom_tile(color = "white", size = 0.15),
  theme(axis.text.x = element_blank(),
        axis.ticks = element_blank(),
        axis.title.x = element_blank(),
        axis.line.x = element_blank(),
        strip.text = element_blank(),
        strip.background = element_blank(),
        plot.margin = margin(0, 0, 0, 0, "npc")),
  scale_y_discrete(expand = c(0, 0)),
  labs(y = ""),
  guides(fill = FALSE),
  facet_grid(~patient_id, 
             space = "free", scales = "free")
)

comp_label_site <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_supersite, patient_id), 
       aes(tumor_subsite_rx, label_supersite, 
           fill = tumor_supersite)) + 
  scale_fill_manual(values = clrs$tumor_supersite) +
  common_label_layers

comp_label_rx <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_therapy, consensus_signature, patient_id), 
       aes(tumor_subsite_rx, label_therapy, 
           fill = therapy)) + 
  scale_fill_manual(values = c(`post-Rx` = "gold3", `pre-Rx` = "steelblue")) +
  common_label_layers

comp_label_mutsig <- ggplot(distinct(comp_tbl_sample_sort, tumor_subsite_rx, therapy, tumor_supersite, label_mutsig, consensus_signature, patient_id), 
       aes(tumor_subsite_rx, label_mutsig, 
           fill = consensus_signature)) + 
  scale_fill_manual(values = clrs$consensus_signature) +
  common_label_layers

patient_label_tbl <- distinct(comp_tbl_sample_sort, patient_id, .keep_all = T)

comp_label_patient_id <- ggplot(patient_label_tbl, aes(tumor_subsite_rx, label_patient_id)) + 
  scale_fill_manual(values = clrs$consensus_signature) +
  geom_text(aes(tumor_subsite_rx, label_patient_id, label = patient_id)) +
  facet_grid(~patient_id, 
             space = "free", scales = "free") +
  coord_cartesian(clip = "off") + 
  theme_void() +
  theme(strip.text = element_blank(),
        strip.background = element_blank(),
        plot.margin = margin(0, 0, 0, 0, "npc"))

hist_plot_wrapper <- function(x = "nrel") {
  if (x == "nrel") {
    xlab <- paste0("Fraction of cells [%]")
    bw <- 5
  }
  if (x == "log10n") {
    xlab <- paste0("Number of cells")
    bw <- 0.2
  }
  p <- ggplot(comp_tbl_sample_sort) +
    ggridges::geom_density_ridges(
      aes_string(x, "cluster_label", fill = "cluster_label"), color = "black",
      stat = "binline", binwidth = bw, scale = 3) +
    facet_grid(label_supersite~., 
               space = "free", scales = "free") +
    scale_fill_manual(values = clrs$cluster_label[[coi]]) + 
    theme(axis.text.y = element_blank(),
          axis.ticks.y = element_blank(),
          axis.title.y = element_blank(),
          axis.line.y = element_blank(),
          strip.text = element_blank(),
          strip.background = element_blank(),
          plot.margin = margin(0, 0, 0, 0, "npc")) +
    scale_x_continuous(expand = c(0, 0)) +
    scale_y_discrete(expand = c(0, 0)) +
    guides(fill = FALSE) +
    labs(x = xlab)
  if (x == "log10n") p <- p + expand_limits(x = c(0, 3)) + 
    scale_x_continuous(expand = c(0, 0), 
                       labels = function(x) parse(text = paste("10^", x)))
  return(p)
}

pcomp1 <- comp_plot_wrapper("n")
pcomp2 <- comp_plot_wrapper("nrel")

pcomp_grid <- plot_grid(comp_label_patient_id, 
                        pcomp1, pcomp2, 
                        comp_label_site, comp_label_rx, comp_label_mutsig,
                        ncol = 1, align = "v", 
                        rel_heights = c(0.15, 0.33, 0.33, 0.06, 0.06, 0.06))

phist1 <- hist_plot_wrapper("log10n")

pcomp_hist_grid <- ggdraw() +
  draw_plot(pcomp_grid, x = 0.01, y = 0, width = 0.85, height = 1) +
  draw_plot(phist1, x = 0.87, y = 0.05, width = 0.12, height = 0.8)

pcomp_hist_grid

# ggsave(paste0("_fig/02_composition_v6_",coi,".pdf"), pcomp_hist_grid, width = 10, height = 2)

3.4.3 site specific cluster enrichment

comp_tbl_z <- comp_tbl_sample_sort %>% 
  filter(therapy == "pre-Rx",
         !(tumor_supersite %in% c("Ascites", "Other"))) %>% 
  group_by(patient_id, cluster_label) %>% 
  arrange(patient_id, cluster_label, nrel) %>% 
  mutate(rank = row_number(nrel),
         z_rank = scales::rescale(rank)) %>% 
  mutate(mean_nrel = mean(nrel, na.rm = T),
         sd_nrel = sd(nrel, na.rm = T),
         z_nrel = (nrel - mean_nrel) / sd_nrel) %>% 
  ungroup()

ggplot(comp_tbl_z) +
  geom_boxplot(aes(tumor_supersite, z_nrel, color = tumor_supersite), 
               outlier.shape = NA) +
  geom_point(aes(tumor_supersite, z_nrel, color = tumor_supersite), position = position_jitter(), size = 0.1) +
  facet_grid(~cluster_label, scales = "free_x", space = "free_x") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
        aspect.ratio = 5) +
  scale_color_manual(values = clrs$tumor_supersite)

ggplot(comp_tbl_z) +
  geom_boxplot(aes(tumor_supersite, z_rank, color = tumor_supersite), 
               outlier.shape = NA) +
  geom_point(aes(tumor_supersite, z_rank, color = tumor_supersite), position = position_jitter(), size = 0.1) +
  facet_grid(~cluster_label, scales = "free_x", space = "free_x") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
        aspect.ratio = 5) +
  scale_color_manual(values = clrs$tumor_supersite)

4 session info

devtools::session_info()

## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value                       
##  version  R version 3.6.2 (2019-12-12)
##  os       Debian GNU/Linux 10 (buster)
##  system   x86_64, linux-gnu           
##  ui       X11                         
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  ctype    en_US.UTF-8                 
##  tz       Etc/UTC                     
##  date     2021-02-25                  
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
##  package      * version    date       lib
##  ape            5.3        2019-03-17 [2]
##  assertthat     0.2.1      2019-03-21 [2]
##  backports      1.1.10     2020-09-15 [1]
##  bibtex         0.4.2.2    2020-01-02 [2]
##  Biobase        2.46.0     2019-10-29 [2]
##  BiocGenerics   0.32.0     2019-10-29 [2]
##  bitops         1.0-6      2013-08-17 [2]
##  broom          0.7.2      2020-10-20 [1]
##  callr          3.4.2      2020-02-12 [1]
##  caTools        1.17.1.4   2020-01-13 [2]
##  cellranger     1.1.0      2016-07-27 [2]
##  cli            2.0.2      2020-02-28 [1]
##  cluster        2.1.0      2019-06-19 [3]
##  codetools      0.2-16     2018-12-24 [3]
##  colorblindr  * 0.1.0      2020-01-13 [2]
##  colorspace   * 1.4-2      2019-12-29 [2]
##  cowplot      * 1.0.0      2019-07-11 [2]
##  crayon         1.3.4      2017-09-16 [1]
##  data.table     1.12.8     2019-12-09 [2]
##  DBI            1.1.0      2019-12-15 [2]
##  dbplyr         2.0.0      2020-11-03 [1]
##  desc           1.2.0      2018-05-01 [2]
##  devtools       2.2.1      2019-09-24 [2]
##  digest         0.6.25     2020-02-23 [1]
##  dplyr        * 1.0.2      2020-08-18 [1]
##  ellipsis       0.3.1      2020-05-15 [1]
##  evaluate       0.14       2019-05-28 [2]
##  fansi          0.4.1      2020-01-08 [2]
##  farver         2.0.3      2020-01-16 [1]
##  fitdistrplus   1.0-14     2019-01-23 [2]
##  forcats      * 0.5.0      2020-03-01 [1]
##  formattable    0.2.0.1    2016-08-05 [1]
##  fs             1.5.0      2020-07-31 [1]
##  future         1.15.1     2019-11-25 [2]
##  future.apply   1.4.0      2020-01-07 [2]
##  gbRd           0.4-11     2012-10-01 [2]
##  gdata          2.18.0     2017-06-06 [2]
##  generics       0.0.2      2018-11-29 [2]
##  ggplot2      * 3.3.2      2020-06-19 [1]
##  ggrepel        0.8.1      2019-05-07 [2]
##  ggridges       0.5.2      2020-01-12 [2]
##  globals        0.12.5     2019-12-07 [2]
##  glue           1.3.2      2020-03-12 [1]
##  gplots         3.0.1.2    2020-01-11 [2]
##  gridExtra      2.3        2017-09-09 [2]
##  gtable         0.3.0      2019-03-25 [2]
##  gtools         3.8.1      2018-06-26 [2]
##  haven          2.3.1      2020-06-01 [1]
##  hms            0.5.3      2020-01-08 [1]
##  htmltools      0.5.1.1    2021-01-22 [1]
##  htmlwidgets    1.5.1      2019-10-08 [2]
##  httr           1.4.2      2020-07-20 [1]
##  ica            1.0-2      2018-05-24 [2]
##  igraph         1.2.5      2020-03-19 [1]
##  irlba          2.3.3      2019-02-05 [2]
##  jsonlite       1.7.1      2020-09-07 [1]
##  KernSmooth     2.23-16    2019-10-15 [3]
##  knitr          1.26       2019-11-12 [2]
##  labeling       0.3        2014-08-23 [2]
##  lattice        0.20-38    2018-11-04 [3]
##  lazyeval       0.2.2      2019-03-15 [2]
##  leiden         0.3.1      2019-07-23 [2]
##  lifecycle      0.2.0      2020-03-06 [1]
##  listenv        0.8.0      2019-12-05 [2]
##  lmtest         0.9-37     2019-04-30 [2]
##  lsei           1.2-0      2017-10-23 [2]
##  lubridate      1.7.9.2    2020-11-13 [1]
##  magrittr     * 2.0.1      2020-11-17 [1]
##  MASS           7.3-51.5   2019-12-20 [3]
##  Matrix         1.2-18     2019-11-27 [3]
##  memoise        1.1.0      2017-04-21 [2]
##  metap          1.2        2019-12-08 [2]
##  mnormt         1.5-5      2016-10-15 [2]
##  modelr         0.1.8      2020-05-19 [1]
##  multcomp       1.4-12     2020-01-10 [2]
##  multtest       2.42.0     2019-10-29 [2]
##  munsell        0.5.0      2018-06-12 [2]
##  mutoss         0.1-12     2017-12-04 [2]
##  mvtnorm        1.0-12     2020-01-09 [2]
##  nlme           3.1-143    2019-12-10 [3]
##  npsurv         0.4-0      2017-10-14 [2]
##  numDeriv       2016.8-1.1 2019-06-06 [2]
##  pbapply        1.4-2      2019-08-31 [2]
##  pillar         1.4.6      2020-07-10 [1]
##  pkgbuild       1.0.6      2019-10-09 [2]
##  pkgconfig      2.0.3      2019-09-22 [1]
##  pkgload        1.0.2      2018-10-29 [2]
##  plotly         4.9.1      2019-11-07 [2]
##  plotrix        3.7-7      2019-12-05 [2]
##  plyr           1.8.5      2019-12-10 [2]
##  png            0.1-7      2013-12-03 [2]
##  prettyunits    1.1.1      2020-01-24 [1]
##  processx       3.4.2      2020-02-09 [1]
##  progeny      * 1.11.3     2020-10-22 [1]
##  ps             1.3.2      2020-02-13 [1]
##  purrr        * 0.3.4      2020-04-17 [1]
##  R.methodsS3    1.7.1      2016-02-16 [2]
##  R.oo           1.23.0     2019-11-03 [2]
##  R.utils        2.9.2      2019-12-08 [2]
##  R6             2.4.1      2019-11-12 [1]
##  RANN           2.6.1      2019-01-08 [2]
##  rappdirs       0.3.1      2016-03-28 [2]
##  RColorBrewer   1.1-2      2014-12-07 [2]
##  Rcpp           1.0.4      2020-03-17 [1]
##  RcppAnnoy      0.0.16     2020-03-08 [1]
##  RcppParallel   4.4.4      2019-09-27 [2]
##  Rdpack         0.11-1     2019-12-14 [2]
##  readr        * 1.4.0      2020-10-05 [1]
##  readxl       * 1.3.1      2019-03-13 [2]
##  rematch        1.0.1      2016-04-21 [2]
##  remotes        2.1.0      2019-06-24 [2]
##  reprex         0.3.0      2019-05-16 [2]
##  reshape2       1.4.3      2017-12-11 [2]
##  reticulate     1.14       2019-12-17 [2]
##  rlang          0.4.8      2020-10-08 [1]
##  rmarkdown      2.0        2019-12-12 [2]
##  ROCR           1.0-7      2015-03-26 [2]
##  rprojroot      1.3-2      2018-01-03 [2]
##  RSpectra       0.16-0     2019-12-01 [2]
##  rstudioapi     0.11       2020-02-07 [1]
##  rsvd           1.0.3      2020-02-17 [1]
##  Rtsne          0.15       2018-11-10 [2]
##  rvest          0.3.6      2020-07-25 [1]
##  sandwich       2.5-1      2019-04-06 [2]
##  scales         1.1.0      2019-11-18 [2]
##  sctransform    0.2.1      2019-12-17 [2]
##  SDMTools       1.1-221.2  2019-11-30 [2]
##  sessioninfo    1.1.1      2018-11-05 [2]
##  Seurat       * 3.1.2      2019-12-12 [2]
##  sn             1.5-4      2019-05-14 [2]
##  stringi        1.5.3      2020-09-09 [1]
##  stringr      * 1.4.0      2019-02-10 [1]
##  survival       3.1-8      2019-12-03 [3]
##  testthat       2.3.2      2020-03-02 [1]
##  TFisher        0.2.0      2018-03-21 [2]
##  TH.data        1.0-10     2019-01-21 [2]
##  tibble       * 3.0.4      2020-10-12 [1]
##  tidyr        * 1.1.2      2020-08-27 [1]
##  tidyselect     1.1.0      2020-05-11 [1]
##  tidyverse    * 1.3.0      2019-11-21 [2]
##  tsne           0.1-3      2016-07-15 [2]
##  usethis        1.5.1      2019-07-04 [2]
##  uwot           0.1.5      2019-12-04 [2]
##  vctrs          0.3.5      2020-11-17 [1]
##  viridis      * 0.5.1      2018-03-29 [2]
##  viridisLite  * 0.3.0      2018-02-01 [2]
##  withr          2.3.0      2020-09-22 [1]
##  xfun           0.12       2020-01-13 [2]
##  xml2           1.3.2      2020-04-23 [1]
##  yaml           2.2.1      2020-02-01 [1]
##  zoo            1.8-7      2020-01-10 [2]
##  source                                 
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## 
## [1] /home/uhlitzf/R/lib
## [2] /usr/local/lib/R/site-library
## [3] /usr/local/lib/R/library

MSK SPECTRUM freeze major subset deep dives

Florian Uhlitz

2021-02-25

1 Cluster markers

1.1 Major Ovarian.cancer.super markers for cell assign

1.2 Subtype currated markers

1.3 Subtype cluster markers

2 Clusters

2.1 sizes

2.2 UMAP

3 Filtering out doublet and mito high clusters

3.1 UMAP

3.2 add module scores and pathway scores

3.3 marker heatmap

3.4 composition

3.4.1 per site

3.4.2 per sample

3.4.3 site specific cluster enrichment

4 session info